BIGDATA: Small DA Social Behavior Driven Modeling and Optimization of Information

描述(由申请者提供)：问题：大规模的社交媒体和社交互动数据，如推文、博客、论坛，变得越来越容易获得。信息在社交网络和社交媒体上传播的模式通常是隐藏的。对这些历史的社会互动进行建模，为理解和优化社会网络中的信息传播提供了巨大的潜力。这类模型也有实际影响，例如。促进保健讨论论坛中的活动，并加快在科学界传播思想。然而，以前的社会网络分析方法大多侧重于对网络行为的定性和宏观的解释性分析，而不是定量的和微观的预测模型。这些模型很难用于后续的信息传播优化和管理。因此，迫切需要一种稳健的、可预测的建模框架，利用大规模的历史社会交互数据，并能够适应社会交互的复杂性和异构性。这个项目的目标是开发一套健壮的 基于复杂噪声交互数据的机器学习方法，用于对信息扩散过程进行建模和优化。它由四个部分组成：(I)开发一个新的概率框架来对社会网络中的事件级联进行建模和推理；(Ii)开发非参数核方法来捕捉社会交互的复杂性和异质性；(Iii)开发高效的在线/批量优化算法来从大数据集中估计扩散模型；以及(Vi)利用估计模型的预测来优化信息传播和促进社会互动。技术创新和优点：我们将创新地使用事件历史分析，通常用于社交网络环境中的医疗数据分析。这为我们提供了一个处理我们项目中所有四个方面的原则性和总体性框架。事件历史分析和核方法的结合还揭示了信息扩散建模问题和分组套索统计估计问题之间的联系，使我们能够将最近发展起来的稀疏恢复理论引入到信息扩散渠道发现等社会网络问题中，并形式化地研究这种恢复的条件和统计保证。相关性(见说明书)：我们提议的研究在健康讨论论坛中有广泛的应用；由糖尿病手基金会运营的Tu糖尿病将是一个试验床。这个项目有可能提高人们在讨论论坛中的参与度，并为糖尿病患者培养更好的社会公益。提出的研究还结合了事件历史分析、核方法、图形模型和稀疏恢复理论等多个研究领域来研究社会网络问题。

DESCRIPTION (provided by applicant): The Problem: Large-scale social media and social interaction data, such as tweets, blogs, discussion forums, are becoming increasing available. The patterns of information diffusion across social networks and social media are generally hidden. Modeling these historical social interactions, promises great potentials for the understanding and optimization of information diffusion in social networks. Such models also have practical impacts such as. promoting activities in health care discussion forums and accelerate the dissemination of ideas in scientific communities. However, most previous approaches for social network analysis focus on qualitative and macroscopic explanatory analysis of the network behavior, rather than quantitative and microscopic predictive models. It is difficult to make use of these models for subsequent optimization and management of information diffusion. Thus there is a great need for a robust and predictive modeling framework leveraging the large-scale historical social interaction data and can adapt to the complexity and heterogeneity of social interactions. , Aim: The goal of this project is to develop a set of robust machine learning methods for modeling and optimizing the information diffusion processes, based on the complex and noisy interaction data. It consists of a pipeline of four components: (i) develop a novel probabilistic framework for modeling and reasoning about cascades of events in social networks; (ii) develop nonparametric kernel methods to capture the complexity and heterogeneity of social interaction; (iii) develop efficient online/batch optimization algorithms fr estimating the diffusion models from large datasets; and (vi) optimize information diffusion and promote social interaction using the predictions of the estimated models. Technical Innovation and Merit: We will make novel use of event history analysis typically used for medical data analysis in the social network context. This provides us a principled and over-arching framework for addressing all four aspects in our project. The combination of event history analysis and kernel methods also reveals the connection between the information diffusion modeling problem and the grouped lasso statistical estimation problem, allowing us to bring in recently developed sparse recovery theory into social network problems such as discovery of information diffusion channels and formally study the conditions and statistical guarantees for such recovery. RELEVANCE (See instructions): Our proposed research has wide-ranging applications in health discussion forum; TuDiabetes which is operated by the Diabetes Hands Foundation will be a testbed. This project has the potential to improve the engagement of people in the discussion forum and foster better social goods for diabetes patients. The proposed research also bring together several research areas, such as event history analysis, kernel methods, graphical models, and sparsity recovery theory, to study social network problems.